Lab 2 loading library

Reading input

1. Scatter plot dependence of Palmitic on Oleic, linolenic not classed and classed

Analysis: The second graph is easier to analysis than compared to first one, the perception problem highlighted here is the difference in the channel capacity of human preception. Where capacity of distingushing hue > intensity

2. Scatter plot dependence of Palmitic on Oleic, linolenic split by different attributes

## Warning: Using size for a discrete variable is not advised.

Analysis: The discretized Linolenic with color was the easiest to detect boundary. This is becuase the channel capacity of detection is in the order of color>direction>size

3. Scatter plot dependence of Oleic on Eicosenoic, Region split by different attributes

Analysis: Using a factor simply as number assumes that the difference between region is an increment of one, eg: Cat->Dog->Human the difference is one unit among the three, while treating them as factor does not assuming any step increment. The Preattentive pattern emerged here due to distinct colour.

4. Scatter plot dependence of Oleic on Eicosenoic, linoleic not classed and classed and 27 objects

## Warning: Using size for a discrete variable is not advised.

Analysis: Its very hard to distinguish between 27 types of combination due to no clear boundary between the regions, the perception problem demostrated here is the attentive mechanism and no individual feature of the chart (shape, color, size) helps in distinguish the boundary

5. Scatter plot dependence of Oleic on Eicosenoic, linoleic not classed and classed and 27 objects

## Warning: Using size for a discrete variable is not advised.

Analysis: Due to establishment of viusally clear boundary between the regions, the attentive mechanism has no problems scanning through the individual feature of the chart (shape, color, size) which inturns helps in distinguish the boundary

6. piechart of oils vs. region

Analysis: The plot does not conform to the practices under good visualization, it is generally not advised to use piechart because angles are harder to detect than size. Not having clear lables/legend simply adds to more overhead to process the information

7. 2D density contour plot

Analysis: As evident from the two plots the density plot suggests a clusters being formed while the simple scatter plots coloured by region suggests a simpler cluster

Assignment 2

1. Loading the input xlxs file

Analysis: Yes its reasonable to scale the data (perform dimensionality reduction) since more than visualizing more than 4 feaures is not advised.

2. Non mertic MDS

## initial  value 12.061782 
## final  value 12.060912 
## converged

Analysis: None of the axis make a clear boundary between the two Leagues. The outlier teams are as follows: Milwaukee Brewers, Boston Red Sox, Los Angeles Angels, Philadelphia Philies, San Diego Padres.

3. Shepard plot of MDS

Analysis: The datapoints away from the 45degree line(orange) are the the pairs of points where the MDS was not successful.

4. Plotting MDS axis with variables of baseball dataset

Appendix